State of the Art in Data Management for
Precision Medicine & Genomics
March 8, 20172 pm – 3 pm ET
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Agenda• Welcome and Introductions
– Claudia Ellison, Program Director, eHealth Initiative
• Discussion & Comments – Virginia Balcom, Vice President, PHEMI Systems– Jim Buntrock, Vice Chair of Information Management and
Analytics, Mayo Clinic– Josh Peterson, MD, MPH, Vanderbilt University School of
Medicine– Paul Terry, MD,CEO and CTO, PHEMI Systems
• Questions & Answers
Overview of eHealth Initiative
• Since 2001, eHealth Initiative (c6) and the Foundation for eHealth Initiative (c3) have conducted research, education and advocacy to demonstrate the value of technology and innovation in health.
• Serve as the industry leader convening executives from multi-stakeholder groups to identify best practices to transform care through use of health IT
• The missions of the two organizations are the same: to drive improvement in the quality, safety, and efficiency of healthcare through information and technology.
• Our work is centered around the 2020 Roadmap. The primary objective of the 2020 Roadmap is to craft a multi-stakeholder solution to enable coordinated efforts by public and private sector organizations to transform care delivery through data exchange and health IT.
4
Roadmap to Transforming Care
RESEARCH
Information Gathering, Surveys,
Interviews
CONVENE
- Exec Roundtables, Committees, Webinars,
Workgroups
OUTPUTS & RECOMMEND
ATIONS
Guidance, Education, Reports
5
eHealth - Convening Executives to Research & Identify Best Practices
6
• Data Analytics
• Data Access and Privacy
• Interoperability
• Patient and Provider Technology Adoption
eHI Member Meeting & Executive NetworkingMarch 21 – 23, 2017
House of Sweden, 2900 K Street, N.W., WDC
Together Facing the Challenges of Change. eHealth Initiative’s 2017 Annual Conference & Member Meetings will bring together the most influential leaders from across the healthcare
spectrum to discuss critical issues related to the use of data and technology to improve
healthcare for all Americans.
www.ehidc.org/events
State of the Art
in Data Management for
Precision Medicine & Genomics
Virginia Balcom
PHEMI Systems
An eHI research report highlighting the issues, strategies, and
challenges being faced by innovators in precision medicine and
genomics. The findings shared in this report provide insight on
how clinical and genetic data is used and managed, as well as the
challenges providers face in genomics research and precision
medicine.
Copyright © 2017 PHEMI. All rights reserved.8
Agenda
Copyright © 2017 PHEMI. All rights reserved.9
• Highlights from the Report
• Precision Medicine Initiatives at the Mayo Clinic
• Precision Medicine Initiatives at Vanderbilt University
• Common data challenges in Precision Medicine
• Questions and Answers
Our Guest Speakers
Copyright © 2017 PHEMI. All rights reserved.10
Jim BuntrockVice Chair of Information Management and Analytics Mayo Clinic
Josh Peterson, MD, MPH,
Vanderbilt University
School of Medicine
Dr. Paul Terry
CEO & CTO
PHEMI Systems
State of the Art in Data Management for Precision Medicine & Genomics
The Study
• Providers were identified based on criteria that assesses their involvement
in genomics research and use
• Interviewed by eHI to share insights on how they are using big data, their
involvement with genomics and precision medicine, and what
challenges they are facing in managing, storing, using and analyzing the
data.
• Study Objectives
• To understand the state of data management in precision medicine from
research to clinical implementation
• To understand if organizations are adopting newer technologies
(Hadoop, others) to handle the data demands of precision medicine
Copyright © 2017 PHEMI. All rights reserved.11
State of the Art in Data Management for Precision Medicine & Genomics
Study Findings – Innovators leveraging clinical and genomics data to strengthen core competency of caring for the patient
• All of those interviewed are interested in incorporating precision medicine to
bring genomics to the point of care
• Providers are just starting to integrate genomics into clinical practice
• Providers understand that simply collecting genomic data is inadequate and
that to derive value from it, they must turn the data into knowledge that
informs clinical decisions and allows them to deliver personalized care.
Copyright © 2017 PHEMI. All rights reserved.12
State of the Art in Data Management for Precision Medicine & Genomics
Study Findings – Diverse Use Cases Emerging as Pioneering Providers find new
ways to derive value from genomic data
Genetic underpinnings of disorders,
using DNA, plasma, and serum samples from
more than 40,000 patients
Inform medication prescription, based on relationship
between adverse events and genetic variants
medical devices and monitors
Reporting, benchmarking,
predictive analytics
Predict patients likely to develop
cancer for
early intervention
Use age-related macular degeneration risk factors for prevention
Use adverse drug reactions to infer associations between metabolic pathways, drugs, and
acquired disorders
Intermediate decision support with gene annotations
Analyzing very large datasets to identify
clinically significant genetic variations
Matching genes to drugs targeted to that gene for
oncology decision support
Precision Medicine
Initiative Cohort 1 million+ volunteers
D3b Center for Data-Driven Discovery in Biomedicineaddressing pediatric cancer
Moon Shots >165 immunotherapy clinical trials
Immunotherapy at proteomic level
State of the Art in Data Management for Precision Medicine & Genomics
Study Findings – The Top Data Management Challenges Providers are Facing
Sufficient
DataUsability
Accessibility
Store &
manage the
volume
Manage the
breadthKnowledge
Clinical
Integration
• Increasing participation
in clinical trials
• Moving from competing
to collaborating –
sharing data
• Acquiring data in a ready state for analysis
• Extracting value from unstructured data,
non-clinical data, and leveraging metadata
• Adopting new
technologies: Hadoop
• Managing patient
registries
• Shortage of specialized skills,
especially data science skills
• Better tools needed to make the
most use of big data.
• Interoperability with EMRs
• Exploring new data
storage solutions, such as
cloud
• Balancing privacy, appropriate de-
identification, and clinical and research
needs
• Managing risk, management, privacy, and
security
• Making data available for real-time clinical
support
Copyright © 2017 PHEMI. All rights reserved.14
eHealth Initiative
James Buntrock
Vice Chair, Information Technology
Mayo Clinic
Data Management and Analytic Strategies Mayo Clinic Center for Individualized Medicine
3/8/2017
16
• Mayo Clinic works with several health information technology companies
• Mayo Clinic is evaluating PHEMI technology
About me,
• I am a technologist.
• I am not a clinician, geneticist, bioinformatician, or molecular biologist.
Disclosures
The Center for Individualized Medicine will
integrate, develop and deploy new individualized
medicine products and services that continually
differentiate our practice for every life Mayo touches.
©2013 MFMER | slide- 17
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Translational ProgramsFrom Promise to Practice
Clinomics
Epigenomics
Biomarker Discovery
Microbiome
Pharmacogenomics
19
19
Need for a robust Infrastructure
Biobank & Biorepositories
Sequencing
Information Technology
Biomedical Ethics
Biomedical Informatics
Education
Genomics Processing – High Level
Sequencing
• Base calling
• QC
Secondary
• Assembly
• Alignment
• Annotations
Tertiary
• Annotations
• Visualization
• Statistics
Interpretation/
Reporting
• Research
• Clinical
Primary
IT Infrastructure/Data Management(Storage, Workflow, Compute, Interfaces, Delivery)
• Data Generation
Data Management
Phenotype
Genomic Variation
Genomic Annotation
Mayo Knowledge
OMIMHGMDTCGA
1000Genome
DemographicsMedication
DiagnosisSpecimen
MxDOutcomes
DNARNA
Protein
SNVCNV
INDELsTranslocationsMethylation
ExpressionSplice-site
Fusion genesmiRNAsnRNA
AgeGender
…
Confirmed PathogenicVariant Frequency
Previous interpretations
Aspects of Clinical Application of Genomics
Patient / Provider CenteredBig Data / Large Scale Data Management
Specialty ClinicsLarge Data sets
Per Visit/Test Functionality
Visualization for Provider and Patient
Multi-Provider Role (Generalist, Geneticist, Oncologist, Specialist)
Storage and management of trillions of variants
Cross-patient inquiry
Analytic Dashboard
Biomarker Discovery
System of Record
Unique Service Delivery
Focus deployment
Lab Management
Literature OMIM
RNA
PatientTreatment
Diagnosis
DNA
Drug Targets
Epi
Family History
Others like me
Advanced DiagnosticIndividualized Medicine Clinic
Bringing it all together …Therapeutic
Targets
Patient Outcomes
Systems of Genomic Medicine: Pharmacogenomics at VUMC
State of the Art in Data Management for Precision Medicine and Genomics
Josh F. Peterson, MD, MPH March 8th, 2017
Associate ProfessorDepartment of Biomedical Informatics Department of MedicineVanderbilt University Medical Center
No conflict of interest disclosures
Managing Wave of Genomic Data
Reportable / Actionable?
NO
GenomicEscrow
EMR
PHR
Promoted
CDS
Learning Health System
Health System
YES
Sequence Data
Gen
oty
pin
gG
enet
ic
Ris
k
Reactive/Indication TestingPreemptively Tested
Prognostic Flag for Testing
Genotyped for PREDICT
Target Clinics
Clopidogrel Warfarin
PREDICT Pharmacogenomics Model
VKORC1 + CYP2C9
Has genetic risk variant
Exposed to new or recent prescription
CYP2C19 Variant
TPMTVariant
Thiopurines
CYP3A5Variant
Clin
ical
A
pp
licat
ion
Tacrolimus
PREDICT test ordered and genotype results delivered to EMR
Clopidogrel sensitivity: Poor Metabolizer – Reduced Antiplatelet Effect – gene result CYP2C19 *2/*3
VUMC returning patientMale, age 60BMI = 34Prior history of hypertension and atrial fibrillation
During clinic appointment, provider is notified in EMR that patient is likely to be prescribed target drug within 3 years and thus benefit from genotype-tailored prescribing.
PREDICTTest
When writing Rx for clopidogrel, cardiologist caring for a patient after a stent is alerted in the e-prescribing system that patient is a poor metabolizer.
PoorMetabolizer
Patient leaves clinic appointment with Rx for appropriate drug.
1 year later
Clinical Workflow
Nomenclature and Interpretations
Gene Nucleotide variationa: Effect on CYP3A5 protein
CYP3A56986A>G
31611C>TSplicing defect
Tacrolimus and CYP3A5 interaction
CYP3A5 *3/*3 Tacrolimus Poor metabolizer
This result signifies that the patient has
two copies of a non-functional allele (*3).
Patients with this genotype are expected
to require standard tacrolimus dosing.
Please consult a clinical pharmacist for
more specific dosing information.
Result
Genotype &
Phenotype
Interpretation
CPIC Guideline - Tacrolimus
Antiplatelet Drug SelectionWithin E-Prescribing and Based on CYP2C19 variant
https://cdskb.org/
Warfarin Dosing Advisor
PREDICT Results in the Patient Portal
Genomic Medicine Case Studies
https://emerge.mc.vanderbilt.edu
State of the Art in
Data Management for Precision Medicine & Genomics
Dr. Paul Terry
CEO & CTO
PHEMI Systems
Copyright © 2017 PHEMI. All rights reserved.35
State of the Art in
Data Management for Precision Medicine &
Genomics
eHi research report highlighting the issues,
strategies, and challenges being faced by
innovators in precision medicine and
genomics
The report will be sent to all registered attendees
after the webinar.
Report Sponsored by
Copyright © 2017 PHEMI. All rights reserved.36
PHEMI Copyright 2017
Integrating a Wide Variety of Heterogeneous Data
Molecular You Solution
• Early warning system
• Prevent, delay, mitigate
• Quarterly molecular screening
• Grow to 25,000 patients
• 15+ varied data sources
• Integrate “omics” with clinical data
• Longitudinal study
PHEMI Copyright 2017
Ability to Extract Data from Complex Data Sources
Semi-Structured StructuredXML Lab Results Reader
Data Processing Function Analytics-Ready Digital Assets
Source File
Name Visibility Value
Glucose Non PHI 4.82
Patient
PHN
PHI 994-859-9326
Collection
Date
Non PHI 2013-02-06
Facility ID Non PHI BCB Van East
Patient
Name
PHI Sullivan, Ian
PHEMI Copyright 2017
Interactive Genomic Analytics
• Annotate known genetic variations using
reference data sets (ClinVar, dbSNP, UCSC
Known Genes)
• Join genotype data with clinical data
collections and omics reference data
• Analyze data using PHEMI’s Genomics API
• Build interactive visualization using Zeppelin
notebooks
• Use Spark API & Machine Learning library for
advanced analysis and modeling
• Export to R & Bioconductor or external
databases
39
PHEMI CONFIDENTIAL
Integration of Data Science Tools
• Predictive Modeling
• Risk Modeling
• Anomaly Detection
• Categorization
• Semantic Analysis
• Natural Language Processing, etc...
Discussion
Jim BuntrockVice Chair of Information Management and Analytics Mayo Clinic
Josh Peterson, MD, MPH,
Vanderbilt University
School of Medicine
Dr. Paul Terry
CEO & CTO
PHEMI Systems
Thank you for Participating
For more information about eHI and its programs and services, please go to our website at www.ehidc.org or please contact:
• Claudia Ellison
– 202-624-3280
This webinar was made possible through the generosity and support of PHEMI!
Slides are available at www.ehidc.org/resources